ettore, v1

DESCRIPTION

@@ -1,18 +1,18 @@
 Package: scMuffin
-Title: MUlti-Features INtegrative approach for SC data analysis
-Version: 0.33
-Date: 2022-05-06
+Title: MUlti-Features INtegrative approach for single-cell data analysis
+Version: 1.0.0
+Date: 2022-05-30
 Authors@R: 
-     c(person(given = "Noemi",
-           family = "Di Nanni",
-           role = c("aut"),
-           email = "[email protected]",
-           comment = c(ORCID = " 0000-0002-6399-321X")),
-    person(given = "Valentina",
+     c(person(given = "Valentina",
            family = "Nale",
            role = c("aut"),
            email = "[email protected]",
            comment = c(ORCID = "0000-0002-4730-9342")),
+    person(given = "Noemi",
+           family = "Di Nanni",
+           role = c("aut"),
+           email = "[email protected]",
+           comment = c(ORCID = " 0000-0002-6399-321X")),
     person(given = "Alice",
            family = "Chiodi",
            role = c("aut"),

NEWS

@@ -1,3 +1,5 @@
+version 1.0.0
+- first release
 version 0.33
 - various improvements
 version 0.32

README.md

@@ -2,7 +2,9 @@
 
 ## scMuffin - A MUlti-Features INtegrative approach for SC data analysis
 
-Single cell (SC) analysis is crucial to study the complex cellular heterogeneity of solid tumors, which is one of the main obstacles for the development of effective cancer treatments. Such tumors typically contain a mixture of cells with aberrant genomic and expression profiles affecting specific sub-populations that have a pivotal role in cancer progression, whose identification eludes bulk approaches. We present a MUlti-Features INtegrative approach for SC data analysis (scMuffin) that characterizes cell identity on the basis of multiple and complementary criteria. scMuffin provides functions to calculate a series of qualitative and quantitative scores, such as: expression of markers for normal and tumor conditions, pathway activity, cell hierarchy, multipotency state, copy number variations and cell cycle state. Cell-level scores are used for cell cluster annotation and combined to obtain alternative cell clusters. scMuffin integrates any type of cell- or cluster-associated data, and can be used for single-cell multi-omics analyses (e.g. mutations, gene expression). As a proof-of-principle, we studied a public dataset of human gliomas. scMuffin combines several tools to shed light on the identity of tumors cells and spot subtle cell types.
+Single-cell (SC) gene expression analysis is crucial to dissect the complex cellular heterogeneity of solid tumours, which is one of the main obstacles for the development of effective cancer treatments. Such tumours typically contain a mixture of cells with aberrant genomic and transcriptomic profiles affecting specific sub-populations that might have a pivotal role in cancer progression, whose identification eludes bulk RNA-sequencing approaches. We present scMuffin, an R package that enables the characterization of cell identity in solid tumours on the basis of multiple and complementary criteria applied on SC gene expression data. scMuffin provides a series of functions to calculate several different qualitative and quantitative scores, such as: expression of marker sets for normal and tumor conditions, pathway activity, cell state trajectories, CNVs, chromatin state and proliferation state. Thus, scMuffin facilitates the combination of various evidences that can be used to distinguish normal and tumoral cells, define cell identities, cluster cells in different ways, link genomic aberrations to phenotypes and identify subtle differences between cell subtypes or cell states. As a proof-of-concept, we applied scMuffin to a public SC expression dataset of human high-grade gliomas, where we found that some chromosomal amplifications might underlie the invasive tumour phenotype and identified rare quiescent cells that may deserve further investigations as candidate cancer stem cells.
+CONCLUSIONS: The analyses offered by CocolainscMuffin tool and the results achieved in this case study show that our tool helps addressing the main challenges in the bioinformatics analysis of SC expression data from solid tumours.
+
 
 ## Installation